Object Detection Models Flashcards

What is the primary role of the Region Proposal Network (RPN) in Faster R-CNN?

Answer: Propose candidate object bounding boxes

The RPN slides over the feature map and proposes candidate bounding boxes (anchors) that may contain objects.

Which loss function does YOLO use for bounding box coordinate regression?

Answer: Mean squared error (MSE)

Original YOLO uses MSE for coordinate regression, though later versions switched to IoU-based losses.

In SSD (Single Shot MultiBox Detector), what is the purpose of using multiple feature map scales?

Answer: To detect objects of different sizes

SSD predicts detections from feature maps at multiple resolutions, allowing it to handle both small and large objects.

What does 'anchor-free' mean in the context of object detectors like FCOS?

Answer: Bounding boxes are predicted without predefined anchor boxes

Anchor-free detectors like FCOS predict object locations directly from feature map points without relying on predefined anchor boxes.

Which metric is typically used to evaluate object detection performance?

Answer: Mean Average Precision (mAP)

mAP averages the Average Precision across all object classes and IoU thresholds, making it the standard detection metric.

In the Feature Pyramid Network (FPN), how are features from different levels combined?

Answer: By top-down pathway with lateral connections

FPN uses a top-down pathway that upsamples high-level features and adds them to lower-level features via lateral connections.

What problem does Non-Maximum Suppression (NMS) solve in object detection?

Answer: Multiple overlapping predictions for the same object

NMS removes redundant bounding boxes by keeping only the highest-scoring box among overlapping predictions for the same object.